The COVID-19 outbreak has cut China’s supply of and raised the world’s demand for face masks, disinfectants, ventilators, and other critical medical goods. This article studies the economic and political factors that are associated with China’s exports of medical equipment during the first two months of the global pandemic. Regression results show that—controlled for demand factors—countries with stronger past economic ties with China import more critical medical goods from China at both the national level and the level of Chinese provinces. Friendly political relations, such as the twinning of provinces, appear to work as a substitute for pre-existing economic ties at the provincial level. These findings imply that, to secure access to medical equipment in crises, countries are well advised to either diversify their sources or to develop closer relations with Beijing and China’s provinces.

Attempts to constrain the spread of Covid-19 included the temporal reintroduction of travel restrictions and border controls within the Schengen area. While such restrictions clearly involve costs, their benefits have been disputed. We use a new set of daily regional data of confirmed Covid-19 cases from the respective statistical agencies of 18 Western European countries. Our data starts with calendar week 10 (starting 2nd March 2020) and extends to calendar week 17 (ending 26th April 2020), which allows us to test for treatment effects of border controls. Based on PPML methods and a Bayesian INLA approach we find that border controls had a significant effect to limit the pandemic.

This paper reviews the literature on incorporating behavioural elements into epidemiological models of pandemics. While modelling behaviour by forward-looking rational agents can provide some insight into the time paths of pandemics, the non-stationary nature of Susceptible-Infected-Removed (SIR) models of viral spread makes characterisation of resulting equilibria dicult. Here I posit a shortcut that can be deployed to allow for a tractable equilibrium model of pandemics with intuitive comparative statics and also a clear prediction that effective reproduction numbers (that is, R) will tend towards 1 in equilibrium. This motivates taking ^R = 1 as an equilibrium starting point for analyses of pandemics with behavioural agents. The implications of this for the analysis of widespread testing, tracing, isolation and mask-use is discussed.

The goal of this paper is to study the impact of the lockdown policy on voting behaviour, during the COVID-19 pandemic. We focus on France, where a differential lockdown was implemented across departments, based on the local diffusion of the disease. In particular, the country has been divided in two areas, red and green, subject to a “hard” and a “soft” lockdown, respectively. To measure voting behaviour, before and after the policy, we rely on 2020 French municipal elections: the first round took place before the introduction of the restrictions, while the second round was delayed after the end of the lockdown. We estimate a Spatial Regression-Discontinuity-Design model comparing the difference in outcomes between the two electoral rounds, at the border of red and green areas. The main results suggest that lockdown regulations significantly affected electoral outcomes. First, in localities under a harder lockdown, the incumbent's vote share is higher as well as the consensus for Green parties. Second, voter turnout is larger where more stringent restrictions are adopted. These results suggest that lockdown measures strongly lead citizens to rally around the local incumbent politicians.

To better understand the trade-offs at play as US states take measures to slow the spread of COVID-19, we investigate the effectiveness of alternative mitigation strategies using panel data estimates informed by epidemiological models. Our analysis evaluates public health outcomes using estimates of the effective reproduction number (Rt), which must drop below 1.0 to achieve sustained reductions in the infectious population. We fit outcomes for Rt on mitigation measures adopted by states, using daily data from early February through late June. Although all of the measures examined help reduce Rt, their effectiveness varies. Reductions in personal mobility on the scale achieved in April can reduce Rt by about a half and are especially effective when paired with stay-at-home orders. Alternatively, our estimates suggest that the virus could be brought under control using less-costly remediation measures. Those measures would likely involve more testing (at a rate of at least 1.75 million per day, if used in isolation), mask wearing requirements (which, in some specifications, are equivalent to testing 1.1mn persons per day), and restrictions on seated dining (which are effective when masks are not mandated). Finally, our estimates suggest that the US is nowhere near the point where “herd immunity” alone can bring infections under control

The paper introduces voluntary social distancing to the canonical epidemiology model, integrated into a conventional macroeconomic model. The model is extended to include treatment, vaccination, and government-enforced lockdown. Infection-averse individuals face a trade-off between a costly social distancing and the risk of getting infected and losing next-period labor income. We find an individual’s social distancing is proportional to the welfare loss she incurs when moving to the infected compartment. It increases in the individual’s psychological discount factor but decreases in the probability of receiving a vaccination. Quantitatively, a laissez-faire social distancing flattens the infection curve that minimizes the economic damage of the epidemic. A government-enforced social distancing is more effective in flattening the infection curve but has a detrimental effect on the economy.

This paper investigates the “cultural” transmission of the SARS-CoV-2 outbreak. Using data from Germany we observe that in predominantly Catholic regions with stronger social and family ties, the spread and the resulting deaths per capita were much higher compared to non-Catholic ones at the local NUTS-3 level. The result is strengthened with Difference-in-Difference estimates at the regional NUTS-1 level. This finding could help explain the rapid spread and high death toll of the virus in some European countries compared to others. Looking at differences within a specific country in a well identified setting eliminates biases due to different social structures, health care systems, specific policies and measures, and testing procedures for the virus that can confound estimates and hinder comparability across countries. Further, we use individual level data as well as mobility data from mobile devices to investigate potential mechanisms. The results highlight the cultural dimension of the spread and could suggest the implementation of targeted mitigation measures in light of disease outbreaks.

In the COVID-19 pandemic, lockdowns and containment measures were a fundamental tool to control the spread of the virus. In this article, we analyze data from 120 countries seeking to assess the stringency of de jure lockdown policies, comparing them with their de facto compliance and empirically analyzing the determinants of social distancing noncompliance. We find that, from a de jure perspective, almost all the strictest and longest lockdowns took place in emerging or developing economies. However, when analyzing its de facto compliance, we document a generalized and increasing non-compliance over time, which is significantly higher in emerging and developing economies. We show that lockdown compliance declines with time, and is lower in countries with stricter quarantines, lower incomes and higher levels of labor precariousness.

COVID-19 has uprooted many aspects of parents’ daily routines, from their jobs to their childcare arrangements. In this paper, we provide a novel description of how parents in England living in two-parent opposite-gender families are spending their time under lockdown. We find that mothers’ paid work has taken a larger hit than that of fathers’, on both the extensive and intensive margins. We find that mothers are spending substantially longer in childcare and housework than their partners and that they are spending a larger fraction of their paid work hours having to juggle work and childcare. Gender differences in the allocation of domestic work cannot be straightforwardly explained by gender differences in employment rates or earnings. Very large gender asymmetries emerge when one partner has stopped working for pay during the crisis: mothers who have stopped working for pay do far more domestic work than fathers in the equivalent situation do.

The Scandinavian countries of Denmark, Iceland, Norway and Sweden have approached the first months of the 2020 novel coronavirus pandemic with a range of economic and health policies that have resulted in disparate outcomes. Though similar in behavioral norms and institutions, Denmark, Iceland and Norway chose a precautionary approach that formally shut down schools and businesses to protect human health, while Sweden took a Business-As-Usual (BAU) approach aimed at maintaining normal economic and social activities. Iceland and Denmark have further invested in testing, tracking and containing the disease. Economic costs of the pandemic and government fiscal and monetary interventions to reduce their impacts have been dramatic and similar across countries, while Sweden has had the most severe loss of life. Using a panel from the four countries since the beginning of the pandemic, we calculate lives saved from stricter interventions by estimating cases and deaths as functions of behavior and government interventions with a bioeconomic model, then estimating the additional lives lost if these interventions did not occur. Comparison of the countries reveals three important lessons for both policies aimed at the pandemic and broader goals with high uncertainty levels: (1) the precautionary approach can be lowest cost, while still expensive; (2) detection and monitoring (e.g. testing and tracking) are integral to a successful precautionary approach; and (3) expecting tradeoffs between economic activity and health creates a false dichotomy – they are complements not substitutes. Pandemic policy should focus on minimizing expected costs and damages rather than attempting to exchange health and safety for economic well-being.

Using survey responses across nearly 500 listed firms in 10 emerging markets from early April, we find the vast majority of firms were negatively affected by COVID-19. Firms reacted by reducing investment rather than payroll. There is a surprising degree of support vis-à-vis employees, customers, other stakeholders and broader society. Although stock prices initially reacted to the impact of the crisis, delayed stock price reactions suggest evidence of inefficient markets. Furthermore, we find evidence that stakeholder-centric firms experienced lower stock price declines during the crisis drawdown.

Using individual, race-disaggregated, and georeferenced death data collected by the Cook County Medical Examiner, we look at the impact of COVID-19 on African Americans and at its determinants. First, we provide evidence that - as of June 16, 2020 - blacks in Cook County are dying from COVID-19 at a rate 1.3 times higher than their population share. Second, by combining the spatial distribution of mortality with the redlining maps for the Chicago area, we establish that - after the epidemic outbreak - historically lower-graded neighborhoods display a sharper increase in mortality, driven by blacks. Thus, we uncover a persistence influence of the racial segregation induced by the lending practices of the 1930s, by way of a diminished resilience of African Americans to the COVID-19 shock. Such influence is channeled through socioeconomic status and household composition, and magnified in combination with a higher black share.

Two decades after the SARS outbreak, Asia is confronted with COVID-19 which has caused a greater economic impact to the region. In this paper, using China and the ASEAN's experiences of SARS and COVID-19 as a case study, we aim to identify the economic impact of a pandemic that is associated with global production linkages. We construct a novel general equilibrium model of production networks with epidemiological dynamics. Calibrating the model with the OECD inter-country input-output tables for the pre-SARS and pre-COVID-19 periods, and controlling for disease dynamics across years, we find that, in the absence of policy intervention, greater importance of China in the global value chains is associated with greater economic impacts, both within China and in the ASEAN region.

The COVID-19 pandemic has put the global economy under a scanner. India has also been impacted by the pandemic and as a result, policymakers have undertaken significant set of measures to address the challenge. In this context, using daily state-level data, we utilize the staggered timing of the implementation of lockdown to ascertain its impact on the number of Covid19 cases. Our analysis appears to suggest that notwithstanding the lockdown, the number of Covid19 cases increased by 80% and furthermore, there was a differential impact across states, depending on their extent of health preparedness. Robustness tests support these findings.

COVID-19 hit firms by surprise. In a high frequency, representative panel of German firms, the business outlook declined and business uncertainty increased only when the spread of the COVID-19 pandemic led to domestic policy changes: The announcement of nation-wide school closures on March 13 caused by far the largest change in business perceptions. In contrast, business perceptions hardly reacted to any other potential source of information: Firms did not learn from foreign policy measures, even if they relied on inputs from China or Italy. The local, county-level spread of COVID-19 cases affected expectations and uncertainty, albeit to a much lesser extent than the domestic policy changes.

In order to get the COVID-19 pandemic under control, most governments around the globe have adopted some sort of containment policies. In the light of the enormous costs of these policies, in many countries highly controversial discussions on the adequacy of the chosen policies evolved. We contribute to this discussion by evaluating three waves of containment measures adopted by the German government. Based on a spatio-temporal endemic-epidemic model we show that in retrospective, only the first wave of containment measures clearly contributed to flattening the curve of new infections. However, a real-time analysis using the same empirical model reveals that based on the then available information, the adoption of additional containment measures was warranted. Moreover, our spatio-temporal analysis shows that a one-size-fits-all policy, as it was adopted in Germany on the early stages of the epidemic, is not optimal.

We propose a simple method based on firms' balance sheets and sectoral predictions of sales growth to determine the firms that will become illiquid month by month as the Covid-19 crisis unfolds. We apply the method to the population of Italian incorporated businesses to the end of 2020. We find that at the peak, around 200,000 companies employing 3.3 million workers would become illiquid. The progression is fast, with 180,000 firms turning illiquid already by April. The liquidity shortage, defined as the "negative" liquidity stock of illiquid firms, amounts to 72 billion. We evaluate the Italian government liquidity decree, which provides guarantees for bank loans under four different facilities of increasing complexity. Assuming that firms have access to all the facilities, almost all firms are able to cover their liquidity shortfalls. The issue is the speed of implementation: the facilities supplying more liquidity are more complex to administrate, and many firms require these facilities to cover their liquidity shortfalls. Overall, we conclude that even in the case of a second wave after the summer, which would increase the liquidity shortfall substantially, firms' liquidity needs are manageable under the current schemes of liquidity provision.

Sharp changes in consumer expenditure may bias inflation during the Covid-19 pandemic. Using public data from debit card transactions, I quantify these changes in consumer spending, update CPI basket weights and construct an alternative price index to measure the effect of the Covid-induced weighting bias on the Swiss consumer price index. I find that inflation was higher during the lock-down than suggested by CPI inflation. The annual inflation rate of the Covid price index was -0.4% by April 2020, compared to -1.1% of the equivalent CPI. Persistent “low-touch” consumer behaviour can further lead to inflation being underestimated by more than a quarter of a percentage point until the end of 2020.

This paper examines the individual records of patients treated for COVID-19 during the early phase of the epidemic in Ontario. We trace out daily transitions of patients through medical care of different intensity and address the right truncation in the database. We also examine the sojourn times and reveal duration dependence in the treatments for COVID-19. The transition model is used to estimate and forecast the counts of patients treated for COVID-19 in Ontario, while accommodating the right truncation and right censoring in the sample. This research is based on the Public Health Ontario (PHO) dataset from May 07, 2020.

How does the nature of work – teleworkability and contact intensity – shape the distribution of health, labor income, and unemployment risks, created by the COVID-19 pandemic? To answer this question, we consider two contexts. First, we show that the existing spousal nature-of-work-based occupational sorting in the United States matters for the distribution of these risks. In particular, we show that it mitigates the risk of catching COVID-19 through intra-household contagion relative to the case of zero sorting. Furthermore, we show that it creates a larger fraction of couples, who are excessively exposed to labor income and unemployment risks, relative to the case of zero sorting. Second, we document that teleworkable occupations require higher education and experience levels as well as greater cognitive, social, character, and computer skills relative to non-teleworkable occupations. This discrepancy affects labor income and unemployment risks by increasing the likelihood of skill mismatch for newly unemployed workers. Our results imply that the current economic downturn may have long-run effects on employment prospects and earnings of workers who had non-teleworkable or high-contact-intensity jobs at the onset of the COVID-19 outbreak. We discuss the relevant policy implications and associated policy constraints that follow from our findings.

This paper compares the performance of safe haven assets during two stressful stock market regimes – the 2008 Global Financial Crisis (GFC) and COVID-19 pandemic. Our analysis across the ten largest economies in the world shows that the traditional choice, gold, acts as a safe haven during the GFC but fails to protect investor wealth during COVID. Our results suggest that investors might have lost trust in gold. Furthermore, silver does not serve as a safe haven during either crisis, while US Treasuries and the Swiss Franc generally act as strong safe havens during both crises. The US dollar acts as a safe haven during the GFC for all the countries except for the United States, but only for China and India during COVID. Finally, Bitcoin does not serve as a safe haven for all countries during COVID; however, the largest stablecoin, Tether, serves as a strong safe haven. Thus, our results suggest that, during a pandemic, investors should prefer liquid and stable assets rather than gold.

We document a decline in mental well-being after the onset of the Covid-19 pandemic in the UK. This decline is more than twice as large for women as for men. We seek to explain this gender gap by exploring gender differences in: family and caring responsibilities; financial and work situation; social engagement; health situation, and health behaviours, including exercise. We discuss two dimensions of gender differences, the extent to which particular circumstances relate to well-being and the share of individuals facing a given circumstance. Overall, we find that differences in family and caring responsibilities can explain a part of the gender gap, but the bulk is explained by social factors such as loneliness. Other factors such as financial difficulties or age are similarly distributed across genders and thus play little role in explaining the gap.

This paper uses a survey representative of the UK online population to assess the willingness to accept loss of certain goods. We had conducted an initial survey in February, focusing on ‘free’ online goods and some potential substitutes and comparators. Consistent with other contingent valuation studies, consumers on average assigned valuations to many of these goods, particularly when benchmarked against revenue figures for the services. Our pilot studies, discussed in a forthcoming paper, also suggested that the actual valuations are not well anchored, but the methodology can give consistent rankings among goods. It is also a useful way to assess changes in valuations. Repeating the survey in May, during the UK, lockdown, we observed significant changes in the valuations of different goods and services, with some large differences by age and gender. In this sense the lockdown has acted as a natural experiment testing for the extent to which digital goods and physical goods are substitutes. These valuation changes may indicate which services are most valuable in a post-pandemic world where more activity takes place online. They also provide important, policy-relevant insights into distributional questions

We study the market reactions following staggered lockdown events across U.S. states during Covid-19. We find that returns on firms located in lockdown states are higher following the lockdown. We interpret these market reactions as reflecting updated beliefs of market participants in the light of events that follow the lockdowns, such as compliance with stay-at-home orders. The effect is (a) only significant when the firm’s county has a high number of infections, (b) larger for firms in essential industries, and (c) larger for states with Democratic trifecta. While lockdown extension announcements are associated with negative market reactions, the reaction is still positive when the county’s number of infections is high. These findings suggest that the market perceives Non-Pharmaceutical Interventions, when effective, to be beneficial for businesses.

The literature has long documented social capital as a key social determinant of health. However, because personal social interactions are implicated in the spread of viral infections, areas with high levels of social capital may have been especially at risk during the early phases of the COVID-19 pandemic when spread could not be halted by behavioral changes. We analyzed data from US counties on laboratory-confirmed COVID-19 cases and COVID-19 deaths and relate county level social capital with the number of days it took a county to reach 10 or 15 cases (from January 22) and with case fatality rate in the county between January 22 and May 8 2020. From January 22 on average it took 68 days for a county to reach at least 10 COVID-19 cases. Disease spread was faster the higher the social capital in a community. In counties with average levels of social capital 10 cases were identified by March 29, but in counties with social capital one SD above the average 10 cases were identified by March 26. The difference is equivalent to the difference estimated across two counties that differ in population density by 12,000 people per square mile. Other things being equal we estimate lower case fatality in higher social capital counties, with a reduction of between 0.2% and 0.4% points per SD difference in social capital. As governments lift mandatory social distancing, social capital may play a key role as a social determinant of health.

This paper shows that the labour market opportunities available to an agent has a significant bearing on how that agent experiences the outbreak of an epidemic. I consider two types of labour (i) market labour that can only produce output in close physical proximity, and (ii) remote labour that can produce output at a distance. This paper develops a Two Agent New Keynesian model extended to include an epidemic bloc and dual feedback between economic decisions and the evolution of the epidemic. I show that an agent restricted to only supply market labour experiences higher death rates vis-à-vis their share of the population, and suffers larger declines in labour and consumption over the course of the epidemic. Post-epidemic, these agents are significantly worse off than their counterparts who can work from home and hence a more unequal society emerges. I then show that simple containment policies, while leading to larger losses in economic prosperity as measured by output loss, can significantly reduce death rates across the population, bring the death rates of the two groups closer together, and reduce the inequality that emerges post epidemic.

We study planned price changes in German firm-level survey data to infer the relative importance of supply and demand during the Covid-19 pandemic. Supply and demand forces coexist, but demand deficiencies dominate in the short run. Quarter-on-quarter producer price inflation is predicted to decline by as much as 1.5 percentage points through August 2020. These results imply a role for demand stimulus policy to buffer the Covid-19 economic crisis.

We document the magnitudes of and mechanisms behind socioeconomic differences in travel behavior during the COVID-19 pandemic. We focus on King County, Washington, one of the first places in the U.S. where COVID-19 was detected. We leverage novel and rich administrative and survey data on travel volumes, modes, and preferences for different demographic groups. Large average declines in travel, and in public transit use in particular, due to the pandemic and related policy responses mask substantial heterogeneity across socioeconomic groups. Travel intensity declined considerably less among less-educated and lower-income individuals, even after accounting for mode substitution and variation across neighborhoods in the impacts of public transit service reductions. The relative inability of less-educated and lower-income individuals to cease commuting explains at least half of the difference in travel responses across groups.

Social distancing is important to slow the community spread of infectious disease, but it creates enormous economic and social cost. It is thus important to quantify the benefits of different measures. We study the ban of mass gatherings, an intervention with comparably low cost. We exploit exogenous spatial and temporal variation in NBA and NHL games, which arise due to the leagues' predetermined schedules, and the suspension of the 2019-20 seasons. This allows us to estimate the impact of these mass gatherings on the spread of COVID-19 in affected US counties. One additional mass gathering increased the cumulative number of COVID-19 deaths in affected counties by 13 percent.

We apply the SIR-macro model proposed by Eichenbaum et al. (2020) in its complete version to comparatively study the interaction between economic decisions and COVID-19 epidemics in five different Brazilian states: São Paulo (SP), Amazonas (AM), Ceará (CE), Rio de Janeiro (RJ) and Pernambuco (PE). Our objective is to analyze qualitatively how the main intrinsic differences of each of these states can affect the epidemic dynamics and its consequences. For this purpose, we compute and compare the model for each of the states, both in competitive equilibrium and under optimal containment policy adoption, and analyze the implications of optimal policy adoption. We conclude that the intrinsic characteristics of the five different states imply relevant differences in the general dynamics of the epidemic, in the optimal containment policies, in the effect of the adoption of these policies and the severity of the economic recessions. Our study can serve as an alert for policymakers of countries of huge dimensions and interstate heterogeneity as Brazil for the necessity of discriminating policies by states or regions instead of adopting a single unified policy for the whole country.

Liquidity restrictions on investors, like the redemption gates and liquidity fees introduced in the 2016 money market fund (MMF) reform, are meant to improve financial stability during crisis. However, we find evidence that they may have exacerbated the run on prime MMFs during the Covid-19 crisis. Severe outflows from prime MMFs amid frozen short-term funding markets led the Federal Reserve to intervene with the Money Market Mutual Fund Liquidity Facility (MMLF). By providing “liquidity of last resort,” the MMLF successfully stopped the run on prime MMFs and gradually stabilized conditions in short-term funding markets.

The COVID-19 pandemic has resulted in over 2 billion people in the world affected by lockdowns. This has significant socioeconomic implications, especially in areas such as crime, where police resources are diverted from crime prevention towards enforcing lockdowns. Also, mobility restrictions imposed by lockdowns might make it harder for criminals to find victims. The net effect of these opposite forces is unknown. This study analyzes the effect of lockdowns on criminal activity in the state of Bihar, India. A sharp regression discontinuity design is implemented harnessing the sudden introduction of a state-wide lockdown and novel high-frequency criminal case data. The results show that lockdown decreases aggregate crime by 44 percent. Negative large effects are observed in diverse types of crimes such as murder (61 percent), theft (63 percent), and crimes against women (64 percent), among others. This seems to be driven by the higher search costs faced by criminals. Finally, by exploiting geographic variation in terms of lockdowns' severity across districts, this study shows that relaxing lockdowns' initial restrictions increase crime, but the increment is lower in less restrictive lockdowns than in restrictive ones. While economically-motivated crimes increased, violent crimes were not impacted. This suggests that the economic downturn produced by the lockdown might be driving these effects. Policy recommendations are discussed.

During the COVID-19 crisis, while the world economy suffered the worst crisis since the Great Depression, the reactions of stock markets have raised concerns. Several economists (including some Nobel laureates) have seen these reactions as evidence that stock markets are not fully efficient, while others have emphasized the difficulty of assessing the dramatic flow of information about the pandemic and its economic consequences. In this paper, we assess how stock markets have integrated public information about the COVID-19, the subsequent lockdowns and the policy reactions. Although the COVID-19 shock has been global, not all countries have been impacted in the same way, and they have not reacted in the same way. We take advantage of this strong heterogeneity. We consider a panel of 74 countries with daily information about the health and economic crisis, from January to April 2020. Stock market reaction can be summarized as follows. 1) Stock markets initially ignored the pandemic (until Feb. 21), before reacted strongly to the growing number of infected people (Feb. 23 to Mar. 20), while volatility surged and concerns about the pandemic arose; following the intervention of central banks (Mar. 23 to Apr. 30), however, shareholders no longer seemed troubled by news of the health crisis, as prices rebound all around the world. 2) Country-specific characteristics appear to have had no influence on stock market response. 3) Investors were sensitive to the number of COVID-19 cases in neighbouring and wealthy countries. 4) Credit facilities and government guarantees, lower policy interest rates, and lockdown measures mitigated the decline in domestic stock prices. Overall, these results suggest that stock markets have been less sensitive to each country’ macroeconomic fundamentals prior the crisis, than to their short-term reaction during the crisis. However, our selected variables explain only a small part of the stock market variations, so it is hard to deny that the link between stock price movements and fundamentals have been anything other than loose.

Using longitudinal microdata for the UK over the period 2009-2020 we control for pre-existing previous trends in mental health in order to isolate and quantify the effects of the Covid-19 pandemic. Mental health in the UK worsened by 8.1% on average as a result of the pandemic and by much more for young adults and for women which are groups that already had lower levels of mental health before Covid-19. Hence inequalities in mental health have been increased by the pandemic. Even larger effects are observed for measures of mental health that capture the number of problems reported or the fraction of the population reporting any frequent or severe problems, which more than doubled.

We study liquidity conditions in the corporate bond market since the onset of the COVID-19 pandemic. We find that in mid-March 2020, as selling pressure surged, dealers were wary of accumulating inventory on their balance sheets, perhaps out of concern for violating regulatory requirements. As a result, the cost to investors of trading immediately with a dealer surged. A portion of transactions migrated to a slower, less costly process wherein dealers arranged for trades directly between customers without using their own balance sheet space. Interventions by the Federal Reserve appear to have relaxed balance sheet constraints: soon after they were announced, dealers began absorbing inventory, bid-ask spreads declined, and market liquidity started to improve. Interestingly, liquidity conditions improved for bonds that were eligible for the Fed’s lending/purchase programs and for bonds that were ineligible. Hence, by allowing dealers to unload certain assets from their balance sheet, the Fed’s interventions may have helped dealers to better intermediate a wide variety of assets, including those not directly targeted.  

We use the synthetic control method to analyze the effect of face masks on the spread of Covid-19 in Germany. Our identification approach exploits regional variation in the point in time when face masks became compulsory. Depending on the region we analyse, we find that face masks reduced the cumulative number of registered Covid-19 cases between 2.3% and 13% over a period of 10 days after they became compulsory. Assessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 40%. 

We estimate the impact of non-pharmacological interventions (NPIs) on COVID-19 deaths in Scandinavia. We exploit policy variation between Denmark and Norway on the one hand and Sweden on the other. The former deployed relatively more stringent lockdowns, the latter did not. Our difference-in-differences models show that stricter lockdown policies are associated with fewer COVID-19 deaths.

We nowcast the economic effects of the Covid-19 pandemic and related lockdown measures in the UK and then analyse the distributional and budgetary effects of the estimated individual income shocks, distinguishing between the effects of automatic stabilisers and those of the emergency policy responses. Under conservative assumptions about the exit strategy and recovery phase, we predict that the rescue package will increase the cost of the crisis for the public budget by an additional £26 billion, totalling over £60 billion. However, it will allow to contain the reduction in the average household disposable income to 1 percentage point, and will reduce poverty rate by 1.1 percentage points (at a constant poverty line), with respect to the pre-Covid situation. We also show that this progressive effect is due to the increased generosity of Universal Credit, which accounts for only 20% of the cost of the rescue package.

This paper estimates the drop in profits and the equity shortfall triggered by the Covid-19 shock and the subsequent lockdown, using a representative sample of 80,972 Italian firms. We find that a 3-month lockdown entails an aggregate yearly drop in profits of €170 billion, with an implied equity erosion of €117 billion for the whole sample, and €31 billion for firms that became distressed, i.e., ended up with negative book value after the shock. As a consequence of these losses, about 17% of the sample firms, whose employees account for 8.8% of total employment in the sample (about 800,000 employees), become distressed. Small and mediumsized enterprises (SMEs) are affected disproportionately, with 18.1% of small firms, and 14.3% of medium-sized ones becoming distressed, against 6.4% of large firms. The equity shortfall and the extent of distress are concentrated in the Manufacturing and Wholesale Trading sectors and in the North of Italy. Since many firms predicted to become distressed due to the shock had fragile balance sheets even prior to the Covid-19 shock, restoring their equity to their pre-crisis levels may not suffice to ensure their long-term solvency.

We analyze government interventions to support firms facing liquidity needs during a lockdown in a competitive model of financial intermediation. Banks and firms have legacy balance sheets at the lockdown date. Firms' liquidity needs can be financed by banks that are subject to risk-weighted capital requirements and funded with insured deposits. An increase in firms' overall claims to external investors aggravates moral hazard problems and reduces expected output. The government can support firms directly through transfers or indirectly through guarantees to new bank loans or reductions in the capital requirement. As a result of the diversification of idiosyncratic firm risks conducted by banks, a reduction in the capital requirement only creates costs for the government following negative aggregate shocks that lead to banks' failure. A pecking order on the government policies that maximize output as a function of the government's budget is derived. For low budget, a reduction in capital requirements is optimal and is fully transmitted to firms through increases in banks' leverage. For medium budget, the capital requirement reduction becomes slack and needs be combined with transfers to firms or loan guarantees. For high budget, transfers are strictly necessary. 

We extract aggregate demand and supply shocks for the US economy from real-time survey data on inflation and real GDP growth using a novel identification scheme. Our approach exploits non-Gaussian features of macroeconomic forecast revisions and imposes minimal theoretical assumptions. After verifying that our results for US post-war business cycle fluctuations are largely in line with the prevailing consensus, we proceed to study output and price fluctuations during COVID-19. We attribute two thirds of the decline in 2020:Q1 GDP to a negative shock to aggregate demand. In contrast, regarding the staggeringly large decline in GDP in 2020:Q2, we estimate two thirds of this shock was due to a reduction in aggregate supply. Statistical analysis suggests a slow recovery due to persistent effects of the supply shock, but surveys suggest a somewhat faster rebound with a recovery in aggregate supply leading the way. 

Many countries around the world have implemented stringent containment measures to halt the spread of the 2019 coronavirus disease (Covid-19) and limit the number of fatalities. Though crucial to slow the course of the pandemic, these measures entail large short-term economic costs. This paper tries to quantify these effects using daily data on real-time containment measures implemented by countries around the world as well as daily indicators of economic activity such as Nitrogen Dioxide (NO2) emissions, international and domestic flights, energy consumption, maritime trade, and retail mobility indices. Results suggest that containment measures have had, on average, a very large impact on economic activity—equivalent to a loss of about 15 percent in industrial production over a 30-day period following the implementation of containment measures. Using a novel database on discretionary fiscal and monetary policy measures implemented by countries in response to the crisis, we find that these policy measures have been effective in mitigating some of these costs. Finally, we find that among different types of containment measures, while stay-at-home requirements and workplace closures are the most effective in curbing both infections and deaths, they are also those associated with the largest economic costs.

We analyse how air traffic across countries contributed to the propagation of COVID-19 by fitting a Spatial Durbin-Watson model adapted to local projections. Such a model explicitly accounts for spatial dependence of observations and allows to track the effect of domestic and foreign new infections over time. Our estimates show that air travel-induced cases amount to 8-9% of all confirmed cases on average, and that these infections from abroad came in two waves: in mid-March and the fourth week of March. We also evaluate that air travel restrictions had a marked impact in reducing the progression of the pandemic from April onward. Closing all air traffic 4 weeks earlier could have prevented between 7,000 and 7,800 deaths. Based on standard values of a statistical life and on the latest estimates of GDP loss induced by air travel restrictions, we conclude that, just as social distancing, spatial distancing might be a cost-effective way to tackle COVID-19 in the short run.

Stay-at-home orders (SAHOs) have been implemented in most U.S. states to mitigate the spread of COVID-19. This paper quantifies the short-run impact of these containment policies on search behavior and labor demand for child care. The child care market may be particularly vulnerable to a SAHO-type policy shock, given that many providers are liquidity-constrained. Using plausibly exogenous variation from the staggered adoption of SAHOs across states, we find that online job postings for early care and education teachers declined by 13% after enactment. This effect is driven exclusively by private-sector services. Indeed, hiring by public programs like Head Start and pre-kindergarten has not been influenced by SAHOs. In addition, we find little evidence that child care search behavior among households has been altered. Because forced supply-side changes appear to be at play, our results suggest that households may not be well-equipped to insure against the rapid transition to the production of child care. We discuss the implications of these results for child development and parental employment decisions.

The COVID19 pandemic has caused shocks to the demand for home childcare (with the closure of schools and nurseries) and the supply of home childcare (with many people not working). We collect real-time data on daily lives to document that UK families with young children have been doing the equivalent of a working week in childcare. Women have been doing the greater share, but overall, the gender childcare gap (the difference between the share of childcare done by women and the share done by men) for the additional, post-COVID19 hours is smaller than that for the allocation of pre-COVID19 childcare. However, the amount of additional childcare provided by men is very sensitive to their employment – the allocation has become more equal in households where men are working from home and where they have been furloughed/ lost their job. There are likely to be long-term implications from these changes – potentially negative for the careers of parents of young children; but also, more positively for some families, for sharing the burden of childcare more equally in the future.  

This paper shows that the optimal combination of social distancing and case detection allows for complete and efficient eradication of COVID-19. The first contribution is theoretical. I show that the optimal suppression-policy is a simple function of observable sufficient-statistics, making it easily implementable. I prove that optimal social distancing is the strongest when an outbreak is detected, and then gradually relaxed.  If case detection is sufficiently efficient, social distancing vanishes wholly and quickly; otherwise, it needs to stay in place until a vaccine arrives. The second contribution is quantitative. I find that, if Italy adopts digital contact tracing, total suppression costs only 0.8% of annual GDP. In sharp contrast, under the current detection efficiency, the total cost of suppression amounts to at least 14% of GDP. 

We develop a multiple-events model and exploit within and between country variation in the timing, type and level of intensity of various public policies to study their dynamic effects on the daily incidence of COVID-19 and on population mobility patterns across 135 countries. We remove concurrent policy bias by taking into account the contemporaneous presence of multiple interventions. The main result of the paper is that cancelling public events and imposing restrictions on private gatherings followed by school closures have quantitatively the most pronounced effects on reducing the daily incidence of COVID-19. They are followed by workplace as well as stay-at-home requirements, whose statistical significance and levels of effect are not as pronounced. Instead, we find no effects for international travel controls, public transport closures and restrictions on movements across cities and regions. We establish that these findings are mediated by their effect on population mobility patterns in a manner consistent with time-use and epidemiological factors. 

I use simple correlations and regression analysis to study how the number of confirmed Covid-19 cases and the number of deaths with Covid-19 per 100,000 people is related with the socioeconomic characteristics of local areas in England and Wales. I find that local areas that have larger households, worse levels of self-reported health and a larger fraction of people using public transport have more Covid-19 infections per 100,000 people. For mortality, household size and use of public transport are less important, but there is a clear relation with age, ethnicity and self-reported health. Local areas with an older population, a larger share of black or Asian population and worse levels of self-reported health have more Covid-19 deaths per 100,000 people. The relation between self-reported health and infections and mortality suggests that encouraging a healthy lifestyle can help prevent the spread of infection and reduce mortality. Also, as many countries now begin to relax lockdown measures, policymakers should pay particular attention to reducing the risk of infection in public transport.

The COVID-19 pandemic and social-distancing as well as stay-at-home orders can directly affect mental health and quality of life. In this ongoing project, we analyze rich data from Telefonseelsorge, the largest German emergency helpline service, to better understand the effect of the pandemic and of local lockdown measures on mental health–related helpline contacts. First, looking at Germany–wide changes, we find that overall helpline contacts increase by around 25% in the first week of the lockdown and slowly decrease again after the third lockdown week. Our results suggest that the increase is not driven by financial worries or fear of the virus itself, but reflects heightened loneliness, anxiety, and suicidal ideation. Second, we exploit spatial variation in policies among German federal states to assess whether the effect depends on the stringency of local measures. Preliminary evidence suggests that the average effect is more pronounced in states that implemented stricter measures.

The Austrian ski resort of Ischgl is commonly claimed to be ground zero for the diffusion of the SARS-CoV-2 virus across Germany. Drawing on data for 401 German counties, we find that conditional on geographical latitude and testing behavior by health authorities, road distance to Ischgl is indeed an important predictor of infection cases, but — in line with expectations — not of fatality rates. Were all German counties located as far from Ischgl as the most distant county of Vorpommern-Rügen, Germany would have seen about 48% fewer COVID-19 cases. A simple diffusion model predicts that the absolute value of the distance-to-Ischgl elasticity should fall over time when inter- and intra-county mobility are unrestricted. We test this hypothesis and conclude that the German lockdown measures have halted the spread of the virus.

We use a repeated large-scale survey of households in the Nielsen Homescan panel to characterize how labor markets are being affected by the covid-19 pandemic. We document several facts. First, job loss has been significantly larger than implied by new unemployment claims: we estimate 20 million lost jobs by April 8th, far more than jobs lost over the entire Great Recession. Second, many of those losing jobs are not actively looking to find new ones. As a result, we estimate the rise in the unemployment rate over the corresponding period to be surprisingly small, only about 2 percentage points. Third, participation in the labor force has declined by 7 percentage points, an unparalleled fall that dwarfs the three percentage point cumulative decline that occurred from 2008 to 2016. Early retirement almost fully explains the drop in labor force participation both for those survey participants previously employed and those previously looking for work. We find increases in the fraction of those being retired across the whole age distribution with women and blacks driving a large part of the accelerated retirement.

How do individuals adjust their consumption in response to information disseminated through peers and the social network? Using United States data on consumption, coupled with geographic friendship ties to measure social connectivity, this paper quantifies the role of social networks as a propagation mechanism for understanding aggregate fluctuations in consumption. Using the COVID-19 pandemic as a source of variation, we find that a 10\% rise in cases and deaths in counties connected through the social network is associated with a 0.64\% and 0.33\% decline in consumption expenditures--roughly three to seven times as large as the direct effects of local cases or deaths. Counties more socially connected to epicenter countries of the pandemic also saw a bigger drop in consumption. These effects are concentrated among consumer goods and services that rely more on social-contact, suggesting that individuals incorporate the experiences from their social network to inform their own consumption choices. We are working on incorporating this microeconomic evidence into a heterogeneous agent model and social interaction to study the aggregate demand implications. 

In times of crisis, humans have a tendency to turn to religion for comfort and explanation. The 2020 COVID-19 pandemic is no exception. Using daily data on Google searches for 95 countries, this research demonstrates that the COVID-19 crisis has increased Google searches for prayer (relative to all Google searches) to the highest level ever recorded. More than half of the world population had prayed to end the coronavirus. The rise amounts to 50% of the previous level of prayer searches or a quarter of the fall in Google searches for flights, which dropped dramatically due to the closure of most international air transport. Prayer searches rose at all levels of income, inequality, and insecurity, but not for the 10% least religious countries. The increase is not merely a substitute for services in the physical churches that closed down to limit the spread of the virus. Instead, the rise is due to an intensified demand for religion: We pray to cope with adversity. 

Since the beginning of the COVID-19 crisis, our perception of the world significantly changed. In this paper we show the results of 3 studies that collectively illustrate a novel mechanism through which this has happened. We document the effect of social distancing on our perceptions, through the moderating effect of ambiguity aversion. In experiment 1 we show that ambiguity aversion predicts illusory pattern perception, defined as identifying faces in white noise pictures. In experiment 2 we show that ambiguity aversion also predicts higher cognitive level illusory pattern perception, defined as belief in conspiracy theories. Experiment 3 shows, through two uniquely timed questionnaires, that ambiguity aversion increases significantly from before to after the lockdown (due to the COVID-19 pandemic) for a sample of over 300 subjects. Remarkably, this difference in ambiguity aversion is no longer significant when we control for the drop in regular social contact over this period.
 

Since the first outbreak was reported in Wuhan, China in late December 2019, the 2019 coronavirus disease (COVID-19) has spread to over 200 countries/territories globally. In response, many countries have implemented several containment measures to halt the spread of the virus and limit the number of fatalities. It remains unclear the extent to which these unprecedented measures have been successful. The paper examines this question using daily data on the number of COVID-19 cases and deaths as well as on real-time containment measures implemented by countries around the world. Results suggest that containment measures have been, on average, very effective in flattening the “pandemic curve” and reducing the number of fatalities. These effects have been stronger in countries where containment measures have been implemented faster and have resulted in less mobility—de facto, more social distancing—and in those with lower temperatures, lower population density, a larger share of an elderly population and stronger health systems. Among different types of containment measures, stay-at-home orders seems to have been more effective in reducing the number of deaths. However, these adjustments benefitted mostly highly educated workers and white collars. Overall, low-income individuals faced worse labor market outcomes and suffered higher psychological costs.

We develop an econometric model of consumer panic (or panic buying) during the COVID-19 pandemic. Using Google search data on relevant keywords, we construct a daily index of consumer panic for 54 countries from January to late April 2020. We also assemble data on government policy announcements and daily COVID-19 cases for all countries. Our panic index reveals widespread consumer panic in most countries, primarily during March, but with significant variation in the timing and severity of panic between countries. Our model implies that both domestic and world virus transmission contribute significantly to consumer panic. But government policy is also important: Internal movement restrictions - whether announced by domestic or foreign governments - generate substantial short run panic that largely vanishes in a week to ten days. Internal movement restrictions announced early in the pandemic generated more panic than those announced later. In contrast, travel restrictions and stimulus announcements had little impact on consumer panic.

The economic effects of a pandemic crucially depend on the extent to which countries are connected in global production networks. In this paper we incorporate production barriers induced by COVID-19 shock into a Ricardian model with sectoral linkages,  trade in intermediate goods and sectoral heterogeneity in production. We use the model to quantify the welfare effect of the disruption in production that started in China and quickly spread across the world. We find that the COVID-19 shock has a considerable impact on most economies in the world, especially when a share of the labor force is quarantined. Moreover, we show that global production linkages have a clear role in magnifying the effect of the production shock. Finally, we show that the economic effects of the COVID-19 shock are heterogeneous across sectors, regions and countries, depending on the geographic distribution of industries in each region and country and their degree of integration in the global production network 
 

I modify the basic SEIR model to incorporate demand for health care. The model is used to study the relative effectiveness of policy interventions that include social distancing, quarantine, contact tracing, and random testing. A version of the model that is calibrated to the Ferguson et al (2020) model suggests that permanent, high-intensity social distancing reduces mortality rates and peak ICU demand substantially, but that a policy that relaxes high-intensity social distancing over time in the context of a permanent efficient quarantine regime is even more effective. Adding contact tracing and random testing to this policy further improves outcomes. For the policies considered, employment outcomes are determined by their respective social distancing components, not their quarantine component or health outcomes. Given the uncertainty surrounding the disease parameters, especially the transmission rate of the disease, and the effectiveness of policies, the uncertainty for health outcomes, however, is very large.  

This paper uses a difference-in-differences framework to estimate the causal impact on the mortality rate of non-pharmaceutical interventions (NPIs) used to fight the 1918 inuenza pandemic. The results suggest that NPIs such as school closures and social distancing introduced a trade-off. While they could lower the fatality rate during the peak of the inuenza pandemic, they might also have reduced the herd immunity and significantly increased the death rate in subsequent years. There is no significant association between the implementation of NPIs and cities' growth.

In this paper, I hypothesize that internal migrants are key agents in the diffusion of viruses. Self-isolation and closure of economic activities in outbreak areas generate many people jobless or socially isolated. Recently settled migrants might therefore choose to return to their home towns, thus spreading the virus further. To test the existence and the quantitative importance of this mechanism, I use subnational data for Italy. I use panel data at the regional-daily level and exploit detailed data on individuals' changes of residence between Italian regions before Covid to measure, for each region, the number of potential of return migrants from outbreak areas. The results suggest that regions with more exposed to return migration experienced more Covid deaths throughout nearly all stages of diffusion of the virus. A back-of-the-envelope calculation suggests that, had all regions had the same number of migrants in outbreak areas as the one at the tenth percentile, Italy would have experienced around 2000 fewer Covid deaths, i.e, 22-24 percent fewer deaths than the regions outside the outbreak areas actually experienced. 

We use high-frequency Google search data, combined with data on the announcement dates of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic in U.S. states, to isolate the impact of NPIs on unemployment in an event-study framework. Exploiting the differential timing of the introduction of restaurant and bar limitations, non-essential business closures, stay-at-home orders, large-gatherings bans, school closures, and emergency declarations, we analyze how Google searches for claiming unemployment insurance (UI) varied from day to day and across states. We describe a set of assumptions under which proxy outcomes (e.g., Google searches) can be used to estimate the causal parameter of interest (e.g., share of UI claims caused by NPIs) when data on the outcome of interest (e.g., daily UI claims) are limited. Using this method, we quantify the share of overall growth in unemployment during the COVID-19 pandemic that was directly due to each of these NPIs. We find that between March 14 and 28, restaurant and bar limitations and non-essential business closures could explain 4.4% and 8.5% of UI claims respectively, while the other NPIs did not increase UI claims. 

The Covid-19 crisis has led to a sharp deterioration in firm and bank balance sheets. The government has responded with a massive intervention in corporate credit markets. We study equilibrium dynamics of macroeconomic quantities and prices, and how they are affected by government intervention in the corporate debt markets. We find that the interventions should be highly effective at preventing a much deeper crisis by reducing corporate bankruptcies by about half, and short-circuiting the doom loop between corporate and financial sector fragility. The fiscal costs are high and will lead to rising interest rates on government debt. We propose a more effective intervention with lower fiscal cost. Finally, we study longer-run consequences for firm leverage and intermediary health when pandemics become the new normal

The rapid and dramatic diffusion of the Covid-19 epidemic in Italy was tackled by the Italian government with social distancing measures and with the suspension of all economic activities, except "essential" sectors. A lively policy debate on more refined criteria to choose what activities to allow and to suspend in the future led INAIL (National Institute for Insurance against Accidents at Work) to develop a measure of the risk of contagion in the workplace. In this paper we exploit this novel source of information about the risk of contagion in the workplace to study, for the first time, the crossindustry relationship between the estimated risk of contagion at work and the adoption of robots, in order to test the hypothesis that robotisation may facilitate social distancing and lower the risk of contagion. The analysis, which includes various controls of possible automation-related confounding factors and addresses possible issues of endogeneity, provides evidence that industries employing more robots per worker in production tend to exhibit a lower risk of contagion due to Covid-19. Results and policy implications for the selection of suspension criteria are discussed.

On average, women comprise a smaller share of deaths from Covid-19. However, variation in the share of Covid-19 deaths for women across countries and US States suggests that biological factors cannot fully account for this gender difference. I hypothesize that women’s participation in the workforce is related to women’s share of Covid-19 deaths. I show that the percent of the full-time workforce comprised by women is positively related to the percent of female Covid-19 deaths across countries. I also show that the percent of the full-time workforce comprised by women is positively related to the incidence of various diseases associated with a more severe impact of Covid-19 and the percent of medical doctors and nurses who are women. My results suggest that in countries where women participate more fully in the workforce, women may be more susceptible to Covid-19 due to increased exposure to the virus and a higher risk of developing comorbidities. Future research should be careful to consider social factors when examining gender differences in health outcomes

Governments are starting to ease restrictions to economic activity. The risks of easing these measures too soon, or in misguided ways, are obvious, not only for public health but also for the economy. A world with no lockdown and a pandemic spreading rapidly through the population does not make for a healthy economy; nor, in all likelihood, does a world in which containment measures have to be repeatedly re-instated after being eased prematurely or in sub-optimal ways. We discuss some key economic issues that the UK government needs to face when thinking about how best to get people back into work: we assemble some basic empirical evidence, identify some challenges that policy-makers will need to confront, and discuss some policy considerations

This paper develops a Nash-equilibrium extension of the classic SIR model of infectious-disease epidemiology ("Nash SIR"), endogenizing people's decisions whether to engage in economic activity during a viral epidemic and allowing for complementarity in social-economic activity. An equilibrium epidemic is one in which Nash equilibrium behavior during the epidemic generates the epidemic. There may be multiple equilibrium epidemics, in which case the epidemic trajectory can be shaped through the coordination of expectations, in addition to other sorts of interventions such as stay-at-home orders and accelerated vaccine development. An algorithm is provided to compute all equilibrium epidemics. 

This paper considers a modification of the standard Susceptible-Infected-Recovered (SIR) model of epidemic that allows for different degrees of compulsory as well as voluntary social distancing. It is shown that the fraction of population that self-isolates varies with the perceived probability of contracting the disease. Implications of social distancing both on the epidemic and recession curves are investigated and their trade off is simulated under a number of different social distancing and economic participation scenarios. We show that mandating social distancing is very effective at flattening the epidemic curve but is costly in terms of employment loss. However, if targeted towards individuals most likely to spread the infection, the employment loss can be somewhat reduced. We also show that voluntary self-isolation driven by individual's perceived risk of becoming infected kicks in only towards the peak of the epidemic and has little or no impact on flattening the epidemic curve. Using available statistics and correcting for measurement errors, we estimate the rate of exposure to COVID-19 for 21 Chinese provinces and a selected number of countries. The exposure rates are generally small but vary considerably between Hubei and other Chinese provinces as well as across countries. Strikingly, the exposure rate in Hubei province is around 40 times larger than the rates for other Chinese provinces, with the exposure rates for some European countries being 3-5 times larger than Hubei (the epicenter of the epidemic). The paper also provides country-specific estimates of the recovery rate, showing it to be about 21 days (a week longer than the 14 days typically assumed), and relatively homogeneous across Chinese provinces and for a selected number of countries.

We develop and calibrate a search-theoretic model of the labor market in order to forecast the evolution of the aggregate US labor market during and after the coronavirus pandemic. The model is designed to capture the heterogeneity of the transitions of individual workers across states of unemployment, employment and across different employers. The model is also designed to capture the trade-offs in the choice between temporary and permanent layoffs. Under reasonable parametrizations of the model, the lockdown instituted to prevent the spread of the novel coronavirus is shown to have long-lasting negative effects on unemployment. This is so because the lockdown disproportionately disrupts the employment of workers who need years to find stable jobs.

We provide an initial assessment of the US Paycheck Protection Program by studying the 273 public firms that received a total of $929 million in loans between April 7 - 27, 2020. Despite receiving significant media coverage, these firms comprise 0.3% of the funds disbursed. Using guidelines specified by the US Small Business Administration, we document that about half of public firms were eligible to apply, of which 13% were eventual borrowers. Within the set of eligible firms, public firm borrowers tended to be smaller, have more employees, have fewer investment opportunities, have preexisting debt balances, and be located in a county with COVID-19 cases. Implementing additional eligibility requirements may help target funds towards the most constrained firm.

The Covid-19 pandemic has motivated a myriad of studies and proposals on how economic policy should respond to this colossal shock. But participants in this debate seldom recognize that the health shock is not entirely exogenous. Its magnitude and dynamics themselves depend on economic policies, and the explicit or implicit incentives those policies provide. To illuminate the feedback loops between medical and economic factors we develop a minimal economic model of pandemics. In the model, as in reality, individual decisions to comply (or not) with virus-related public health directives depend on economic variables and incentives, which themselves respond to current economic policy and expectations of future policies. The analysis yields several practical lessons: because policies affect the speed of virus transmission via incentives, public health measures and economic policies can complement each other, reducing the cost of attaining desired social goals; expectations of expansionary macroeconomic policies during the recovery phase can help reduce the speed of infection, and hence the size of the health shock; the credibility of announced policies is key to rule out both self-fulfilling pessimistic expectations and time inconsistency problems. The analysis also yields a critique of the current use of SIR models for policy evaluation, in the spirit of Lucas (1976).

We study the impact of non-pharmaceutical policy interventions (NPIs) like “stay-at-home” orders on the spread of infectious disease. NPIs are associated with slower growth of Covid-19 cases. NPIs “spillover” into other jurisdictions. NPIs are not associated with significantly worse economic outcomes measured by job losses. Job losses have been no higher in US states that implemented “stay-at-home” during the Covid-19 pandemic than in states that did not have “stay-at-home”. All of these results demonstrate that the Covid-19 pandemic is a common economic and public health shock.  The tradeoff between the economy and public health today depends strongly on what is happening elsewhere. This underscores the importance of coordinated economic and public health responses. 

We construct a quantitative model of an economy hit by an epidemic. People differ by age and skill, and choose occupations and whether to commute to work or work from home, to maximize their income and minimize their fear of infection. Occupations differ by wage, infection risk, and the productivity loss when working from home. By setting the model parameters to replicate the progression of COVID-19 in South Korea and the United Kingdom, we obtain three key results. First, government-imposed lock-downs may not present a clear trade-off between GDP and public health, as commonly believed, even though its immediate effect is to reduce GDP and infections by forcing people to work from home. A premature lifting of the lock-down raises GDP temporarily, but infections rise over the next months to a level at which many people choose to work from home, where they are less productive, driven by the fear of infection. A longer lock-down eventually mitigates the GDP loss as well as flattens the infection curve. Second, if the UK had adopted South Korean policies, its GDP loss and infections would have been substantially smaller both in the short and the long run. This is not because Korea implemented policies sooner, but because aggressive testing and tracking more effectively reduce infections and disrupt the economy less than a blanket lock-down. Finally, low-skill workers and self-employed lose the most from the epidemic and also from the government policies. However, the policy of issuing “visas” to those who have antibodies will disproportionately benefit the low-skilled, by relieving them of the fear of infection and also by allowing them to get back to work.

In this paper, we explore different channels to explain the disparities in COVID-19 incidence across New York City neighborhoods. To do so, we estimate several regression models to assess the statistical relevance of different variables such as neighborhood characteristics and occupations. Our results suggest occupations are crucial for explaining the observed patterns, with those with a high degree of human interaction being more likely to be exposed to the virus. Moreover, after controlling for occupations, commuting patterns no longer play a significant role. The relevance of occupations is robust to the inclusion of demographics, with some of them, such as income or the share of Asians, having no statistical significance. On the other hand, racial disparities still persist for Blacks and Hispanics compared to Whites, although their magnitudes are economically small. Additionally, we perform the same analysis over a time window to evaluate how different channels interact with the progression of the pandemic, as well as with the health policies that have been set in place. While the coefficient magnitudes of many occupations and demographics decrease over time, we find evidence consistent with higher intra-household contagion as days go by. Moreover, our findings also suggest a selection on testing, whereby those residents in worse conditions are more likely to get tested, with such selection decreasing over time as tests become more widely available.
 

Older adults and those with underlying medical conditions seem especially vulnerable to the COVID-19 pandemic. The U.S. government’s efforts to contain the infection, on the other hand, have a disproportionate impact on the working age population. To be able to capture the impact of the pandemic and the resulting mitigation efforts on a population that is heterogeneous by age, income and health status, we use an overlapping generations model that mimics the U.S. economy along those dimensions in 2020. We introduce an unexpected COVID-19 shock in the economy and examine the resulting impact on aggregate output, labor supply, savings, and consumption behavior of the different agents. We find that mitigation efforts that target certain age and health groups result in significantly smaller disruptions in the economy. Going forward, introducing subsidies to those with underlying health conditions and/or the elderly to self isolate might prove to be a useful path in opening up the economy.

This study investigates the effectiveness of lockdown and testing in curbing the transmission of Covid-19 infection. Using a combination of data from the European Centre for Disease Prevention, Roser et al. (2020) and Hale et al. (2020), we carried cross-country analysis covering 69 countries across the 5 continents. To take care of the fact that the number of Covid-19 cases strongly depend on its own lag values, we used two-step system GMM for the estimations. Unlike prior studies that measure lockdown in terms of a fixed intervention date, we relied on the stringency index from Hale et al. (2020) that accommodates for the gradual lockdown measures in different countries. We found that an exogenous lockdown significantly affects the number of confirmed cases after 7 days of its implementation and its lag effects are intact even after 21 days of its implementation. A one unit change in the lockdown index decreases the total number of confirmed cases by 0.19 percent. Testing has no significant effects for at least 14 days after its implementation. However, after 21 days of its implementation, its effects become significant with −0.03 to −0.05 elasticity value.

This paper provides a critical review of models of the spread of the coronavirus (SARS-CoV 2) that have been influential in recent policy discussions. It notes potentially important features of the real-world environment that the standard models do not incorporate and discusses reasons why estimating critical parameters is difficult. These limitations may bias forecasts and lead forecasters to overstate confidence in their predictions. They also provide social scientists with opportunities to advance the literature and enable improved policies. This paper also discusses how optimal policies might depend on what is learned from new data and models.

The Covid-19 pandemic has disrupted working life in many ways, the negative consequences of which may be distributed unevenly under lockdown regulations. In this paper, we construct a new set of pandemic-related indices from the Occupational Information Network (O*NET) using factor analysis. The indices capture two key dimensions of job task requirements: (i) the extent to which jobs can be adaptable to work from home; and (ii) the degree of infection risk at workplace. The interaction of these two dimensions help identify which groups of workers are more vulnerable to income losses, and which groups of occupations pose more risk to public health. This information is crucial for both designing appropriate supporting programs and finding a strategy to reopen the economy while controlling the spread of the virus. In our application, we map the indices to the labor force survey of a developing country, Thailand, to analyze these new labor market risks. We document differences in job characteristics across income groups, at both individual and household levels. First, low income individuals tend to work in occupations that require less physical interaction (lower risk of infection) but are less adaptable to work from home (higher risk of income/job loss) than high income people. Second, the positive occupational sorting among low-income couples amplifies these differences at the household level. Consequently, low-income families tend to face a disproportionately larger risk of income/job loss from lockdown measures. In addition, the different exposure to infection and income risks between income groups can play an important role in shaping up the timing and optimal strategies to unlock the economy.

This paper provides evidence on the impact of major epidemics from the past two decades on income distribution. Our results justify the concern that the current pandemic could end up exerting a significant impact on inequality: past events of this kind, even though much smaller in scale, have led to increases in the Gini coefficient, raised the income shares of higher income deciles, and lowered the employment-to-population ratio for those with basic education compared to those with higher education. We provide some evidence that the distributional consequences from the current pandemic may be larger than those flowing from the historical pandemics in our sample.

The COVID-19 pandemic and the subsequent lockdown brought about a massive slowdown of the economy and an unparalleled stock market crash. Using US data, this paper explores how firms with high Environmental and Social (ES) ratings fare during the first quarter of 2020 compared to other firms. We show that stocks with high ES ratings have significantly higher returns, lower return volatilities, and higher trading volumes than other stocks. Firms with high ES ratings and high advertising expenditures perform especially well during the crash. This paper highlights the importance of ES policies in making firms more resilient during a time of crisis.

I develop an extension of a canonical epidemiology model in which the policy in place determines the probability of transmission of an epidemic disease. I use the model to evaluate the effects of isolating symptomatic individuals, of increasing social distancing and of tests of different quality: a poor quality test that can only discriminate between healthy and infected individuals (such as polymerase chain reaction 'PCR' or Rapid Diagnostic Test), and a high quality test that is able to discriminate between immune and vulnerable healthy, and infected individuals (such as a serology test like Neutralization Assay). I find that isolating symptomatic individuals has a large effect at delaying and reducing the pick of infections. The combination of this policy with the poor quality test represents only a negligible improvement, whereas with the high quality test there is an additional delaying and reduction in the pick of infections. Social distancing alone cannot achieve similar effects without incurring in enormous output losses. I explore the combined effect of social distancing at early stages of the epidemic with a following period of tests and find that the best outcome is obtained with a light reduction of human interaction for about three months together with a subsequent test of the population over 40 days.

The paper presents early research on problems that make the use of the basic SIR-model of epidemiology difficult for short- and medium-run policy. The model essentially generalizes the simple exponential model in two respects. First, it considers the structure of the infectious population in more detail and introduces the concept of the "cohort composition kernel" that generalizes the aggregate transmission function and renders the transmission model non-recursive. Second, it shows how policy measures such as testing, social distancing, or quarantine rules can affect this kernel and how this can provide estimates for the impact and lag of nonpharmaceutical policy interventions.
 

To predict the effects of the 2020 U.S. ‘CARES’ act on consumption, we extend a model that matches responses of households to past consumption stimulus packages. The extension allows us to account for two novel features of the coronavirus crisis. First, during the lockdown, many types of spending are undesirable or impossible. Second, some of the jobs that disappear during the lockdown will not reappear when it is lifted. We estimate that, if the lockdown is short-lived, the combination of expanded unemployment insurance benefits and stimulus payments should be sufficient to allow a swift recovery in consumer spending to its pre-crisis levels. If the lockdown lasts longer, an extension of enhanced unemployment benefits will likely be necessary if consumption spending is to recover.

We leverage an event-study research design focused on the seven costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic "unemployment," we show that out-ofsample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the Covid-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty surrounding these estimates and demonstrate their use within a broader forecasting framework for US economic activity.

The British government has been debating how and when to escape from the lockdown without provoking a resurgence of the Covid-19 disease. There is a growing recognition of the damage the lockdown is causing to economic and social life, including deaths and illness amongst the non-infected population. This paper presents a simple cost-benefit analysis based on optimal control theory and incorporating the SIR model of disease propagation. It concludes by presenting some simulations informed by the theoretical discussion. The main conclusions are: (1) the lockdown should be continued for some weeks, and (2) if there is an inexpensive way of reducing the net reproductive rate of the disease to r = 1, this policy should be adopted within a few weeks of exiting lockdown. It is not cost-effective to linger in intermediate stages with more expensive policies designed to keep r well below unity with the hope eradicating the disease.

Motivated by reports in the media suggesting unequal access to Covid-19 testing across incomes, we analyze zip-code level data on the number of Covid-19 tests, test results, and income per capita in New York City. We find that the number of tests administered is evenly distributed across income levels. In particular, the test distribution across income levels is significantly more egalitarian than the distribution of income itself: The ten percent of the city’s population living in the richest zip codes received 11 percent of the Covid-19 tests and 29 percent of the city’s income. The ten percent of the city’s population living in the poorest zip codes received 10 percent of the tests but only 4 percent of the city’s income. At the same time, we find significant disparity in the fraction of tests that come back negative for the Covid-19 disease across income levels: moving from the poorest zip codes to the richest zip codes is associated with an increase in the fraction of negative Covid-19 test results from 38 to 65 percent.

We study how the share of employment that can work from home changes with country income levels. We document that in urban areas, this share is only about 20% in poor countries, compared to close to 40% in rich ones. This result is driven by the self-employed workers: in poor countries their share of employment is large and their occupational composition not conducive to work from home. At the level of the entire country, the share of employment that can work from home in poor countries compared to rich countries depends on farmers' ability to work from home. This finding is due to the high agricultural employment share in poor countries.
 

This paper discusses how to design a policy mix in which a fiscal backstop based on cash transfers is financed through a form of monetization that produces losses in the central bank’s balance sheet, without a permanent increase in the money base. If an independent central bank acts as a long-sighted policymaker, an optimal helicopter monetary policy can be identified. At the same time, if the government in charge is made up of career-concerned politicians and citizens are heterogenous, then the policy mix will produce distributional effects, and conflicts between politicians and central bankers will be likely. Political pressures will arise and the helicopter money option will be unlikely. The framework is applied in a discussion of the economics and politics of issuing COVID-19 perpetual bonds with the European Central Bank as the buyer.

We embed a lockdown choice in a simplified epidemiological model and derive formulas for the optimal lockdown intensity and duration. The optimal policy reflects the rate of time preference, epidemiological factors, the hazard rate of vaccine discovery, learning effects in the health care sector, and the severity of output losses due to a lockdown. In our baseline specification a Covid-19 shock as currently experienced by the US optimally triggers a reduction in economic activity by two thirds, for about 50 days, or approximately 9.5 percent of annual GDP.

Many countries are shutting non-essential sectors of the economy to slow the spread of Covid-19. Older individuals have most to gain from slowing virus diffusion. Younger workers in sectors that are shuttered have the most to lose. In this paper we extend a standard epidemiological model of disease progression to include heterogeneity by age, and multiple sources of disease transmission. We then incorporate the epidemiological block into a multisector economic model in which workers differ by sector (basic and luxury) as well as by health status. We study optimal mitigation policies of a utilitarian government that can redistribute resources across individuals, but where such redistribution is costly. We show that optimal redistribution and mitigation policies interact, and reflect a compromise between the strongly diverging preferred policy paths across the subgroups of the population

In this paper, I examine the feasibility of working from home in developing countries. I take advantage of worker-level data from the STEP survey, which collects comparable information on employment outcomes across ten countries. I use information on workers' tasks to define the feasibility of working from home following Dingel and Neiman (2020). I extend the nascent literature on this topic by providing comparable cross-country evidence on the feasibility of telework. Only 13% of workers in STEP countries could work from home, yet this share ranges from 5.5% in Ghana to 23% in Yunnan (China). The feasibility of working from home is positively correlated with high-paying occupations. Educational attainment, formal employment status and household wealth are positively associated with the possibility of working from home, reflecting the vulnerability of various groups of workers. These relationships remain significant within narrowly defined occupations, yet exhibit heterogeneity across countries. I remark on the importance of rapidly identifying vulnerable workers to design adequate policies to combat the negative employment impacts of COVID-19.

In this paper we argue that endogenous shifts in private consumption behaviour across sectors of the economy can act as a potent mitigation mechanism during an epidemic or when the economy is re-opened after a temporary lockdown. Extending the theoretical framework proposed by Eichenbaum-Rebelo-Trabandt (2020), we distinguish goods by the degree to which they can be consumed at home rather than in a social (and thus possibly contagious) context. We demonstrate that, within the model the "Swedish solution" of letting the epidemic play out without government intervention and allowing agents to shift their sectoral behavior on their own can lead to a substantial mitigation of the economic and human costs of the Covid-19 crisis, avoiding more than 80% of the decline in output and of number of deaths within one year, compared to a model in which sectors are assumed to be homogeneous. For different parameter configurations that capture the additional social distancing and hygiene activities individuals might engage in voluntarily, we show that infections may decline entirely on their own, simply due to the individually rational reallocation of economic activity: the curve not only just flattens, it gets reversed.

As an alternative to structural estimates of the SIR model, this note presents reduced-form time series forecasts of the growth in Covid-19 cases s and fatalities d for several countries where a slowdown has set in. Once a daily threshold of d >100 was crossed, daily growth in the initial unchecked phase was around Δln(d) ∈ [0.2,0.3]. For several countries, growth in fatalities as well as registered cases now shows a sustained decline; Italy and Spain report Δln(d) ≈ 0.03. I present updated ETS forecasts of the endpoint to the current epidemic for the countries in the sample, along with predicted fatalities. As a robustness check, forecasts from 31 March alongside later realizations are included. Results are preliminary and subject to daily revision as the situation is still evolving rapidly. The relative success of the method suggests univariate forecasts as a quick way of assessing resource needs and timelines where the epidemic is still ongoing.
 

We study the response of an economy to an unexpected epidemic and we compare the decentralised equilibrium with the efficient allocation. Households mitigate the spread of the disease by reducing consumption and hours worked. A social planner worries about two externalities: an infection externality and a healthcare congestion externality. Private agents’ mitigation incentives are too weak, especially at early stages while the planner implements drastic and front-loaded mitigation policies. In our calibration, assuming a CFR of 1% and an initial infection rate of 0.1%, private mitigation leads to a 10% drop in consumption and reduces the cumulative death rate from 2.5% of the initially susceptible population to about 2%. The planner reduces the death rate to 0.2% at the cost of an initial drop in consumption of around 40%.
 

The economic downturn caused by the current Covid-19 outbreak has substantial implications for gender equality, both during the downturn and the subsequent recovery. Compared to 'regular' recessions, which affect men’s employment more severely than women’s employment, the employment drop related to social distancing measures has a large impact on sectors with high female employment shares. In addition, closures of schools and daycare centers have massively increased child care needs, which has a particularly large impact on working mothers. The effects of the crisis on working mothers are likely to be persistent, due to high returns to experience in the labour market. Beyond the immediate crisis, there are opposing forces which may ultimately promote gender equality in the labour market. First, businesses are rapidly adopting flexible work arrangements, which are likely to persist. Second, there are also many fathers who now have to take primary responsibility for child care, which may erode social norms that currently lead to a lopsided distribution of the division of labour in house work and child care.

New York City is the hot spot of the Covid-19 pandemic in the United States. This paper merges information on the number of tests and the number of infections at the New York City zip code level with demographic and socioeconomic information from the decennial census and the American Community Surveys. People residing in poor or immigrant neighbourhoods were less likely to be tested; but the likelihood that a test was positive was larger in those neighbourhoods, as well as in neighbourhoods with larger households or predominantly black populations. The rate of infection in the population depends on both the frequency of tests and on the fraction of
positive tests among those tested. The non-randomness in testing across New York City neighbourhoods indicates that the observed correlation between the rate of infection and the socioeconomic characteristics of a community tells an incomplete story of how the pandemic evolved in a congested urban setting.
 

What is the impact of anxiety on vote choice? Building on a well-documented phenomenon in finance, we posit that voters will exhibit a “flight to safety” by turning toward establishment candidates. We test this theory in the context of the Democratic primary election of 2020 by examining changes in the vote shares of Bernie Sanders, a candidate promising disruptive change. We use the outbreak of the novel coronavirus across both space and time to identify a causal effect of the outbreak on voting. By comparing counties with and without reported cases in their local media market, before and after the outbreak of the virus, we show that COVID-19 resulted in diminished support for Sanders as compared to his support in the 2016 election, and interpret this to be the result of COVID-induced anxiety altering vote choice. We test alternative mechanisms, such as differential changes in turnout by age groups more and less supportive of Sanders, selection effects in which areas less supportive of Sanders were more exposed, and the coincident timing of
the outbreak with the Democratic party rallying around Biden. We find little support for these alternative pathways, bolstering our claim that the results are consistent with a political flight to safety. Our findings suggest an as-yet underappreciated preference for “safe” candidates in times of social anxiety.

Social distancing interventions can be effective against epidemics but are potentially detrimental for the economy. Businesses that rely heavily on face-to-face communication or close physical proximity when producing a product or providing a service are particularly vulnerable. There is, however, no systematic evidence on the role of human interactions across different lines
of business and about which will be the most limited by social distancing. In this paper we provide theory-based measures of the reliance of US businesses on human interaction, detailed by industry and geographic location. We find that 49 million workers work in occupations that rely heavily on faceto-face communication or require close physical proximity to other workers. Our model suggests that when businesses are forced to reduce worker contacts by half, they need a 12% wage subsidy to compensate for the disruption in communication. Retail, hotels and restaurants, arts and entertainment and
schools are the most affected sectors. Our results can help target fiscal assistance to businesses that are most disrupted by social distancing.

This study quantifies the economic effect of a possible lockdown of Tokyo to prevent the spread of Covid-19. The negative effect of the lockdown may propagate to other regions through supply chains because of a shortage of supply and demand. Applying an agent-based model to the actual supply chains of nearly 1.6 million firms in Japan, we simulate what would happen to production activities outside Tokyo when production activities that are not essential to citizens’ survival in Tokyo were shut down for a certain period. We find that when Tokyo is locked down for a month, the indirect effect on
other regions would be twice as large as the direct effect on Tokyo, leading to a total production loss of 27 trillion yen in Japan, or 5.3% of its annual GDP. Although the shut down in Tokyo accounts for 21% of the total production in
Japan, the lockdown would result in a reduction in the daily production in Japan of 86% in a month.

We classify the feasibility of working at home for all occupations. About 34% of US jobs, accounting for 44% of overall wages, can plausibly be performed at home. This share varies considerably across cities and industries. We hope our results are useful for understanding the economic impact of social distancing and formulating policy responses. Our classifications could be easiy applied to other countries.

No previous infectious disease outbreak, including the Spanish flu, has impacted the stock market as powerfully as the Covid-19 pandemic. We use text-based methods to develop this point with respect to large daily stock market moves back to 1900 and with respect to overall stock market volatility back to 1985. We also argue that policy responses to the Covid-19 pandemic provide the most compelling explanation for its unprecedented stock market
impact.